A Covid-19 Safety Web Application to Monitor Social Distancing and Mask Detection

Covid-19 Pandemic dipped entire world in sorrow and grief,people are fighting for their lives. Vaccines are being given to people based on priority such as frontline workers, old age, etc. In this situation, the only way to prevent ourselves from getting infected is to follow precautionary measures like social distancing, wearing masks and more. To make everyone follow the precautionary measures effectively, an idea is proposed to monitor social distancing and mask detection. The system is designed using pre-trained models such as DSFD, Mobilenetv2, Resnet50 and YOLOv3 weights and also adding few layers on top of pre-trained models. This helps in detecting people wearing masks or not and calculate the distance between people in a single model by indicating in different colored bounding boxes. It is further made interesting by collaborating the proposed design with the Streamlit framework resulting in a fine Web Application.